Hedge strategies for Bayesian optimization, where a portfolio of acquisition functions is used instead of a single acquisition function, are efficient for single objective algorithms. We present here Hedge Multi-Objective (HedgeMO) algorithm, which uses a portfolio of acquisition functions for multi-objective problems. Hedge algorithms for single-objective problems have regret bounds proven by [1]}, and the same bounds hold for HedgeMO.
